Routine Health Information Systems Research Articles

Effect of Performance-Based Nonfinancial Incentives on Data Quality in Individual Medical Records of Institutional Births: Quasi-Experimental Study.

Despite the potential of routine health information systems in tackling persistent maternal deaths stemming from poor service quality at health facilities during and around childbirth, research has demonstrated their suboptimal performance, evident from the incomplete and inaccurate data unfit for practical use. There is a consensus that nonfinancial incentives can enhance health care providers' commitment toward achieving the desired health care quality. However, there is limited evidence regarding the effectiveness of nonfinancial incentives in improving the data quality of institutional birth services in Ethiopia. This study aimed to evaluate the effect of performance-based nonfinancial incentives on the completeness and consistency of data in the individual medical records of women who availed institutional birth services in northwest Ethiopia. We used a quasi-experimental design with a comparator group in the pre-post period, using a sample of 1969 women's medical records. The study was conducted in the "Wegera" and "Tach-armacheho" districts, which served as the intervention and comparator districts, respectively. The intervention comprised a multicomponent nonfinancial incentive, including smartphones, flash disks, power banks, certificates, and scholarships. Personal records of women who gave birth within 6 months before (April to September 2020) and after (February to July 2021) the intervention were included. Three distinct women's birth records were examined: the integrated card, integrated individual folder, and delivery register. The completeness of the data was determined by examining the presence of data elements, whereas the consistency check involved evaluating the agreement of data elements among women's birth records. The average treatment effect on the treated (ATET), with 95% CIs, was computed using a difference-in-differences model. In the intervention district, data completeness in women's personal records was nearly 4 times higher (ATET 3.8, 95% CI 2.2-5.5; P=.02), and consistency was approximately 12 times more likely (ATET 11.6, 95% CI 4.18-19; P=.03) than in the comparator district. This study indicates that performance-based nonfinancial incentives enhance data quality in the personal records of institutional births. Health care planners can adapt these incentives to improve the data quality of comparable medical records, particularly pregnancy-related data within health care facilities. Future research is needed to assess the effectiveness of nonfinancial incentives across diverse contexts to support successful scale-up.

Operationalising effective coverage measurement in Ethiopia: a qualitative study

Background Efforts to improve the quality of maternal, neonatal and child health services have intensified in Ethiopia. Consistent with global recommendations, measuring only coverage of these health services is no longer sufficient to assess their impact on population health. As a result, interest has grown in effective coverage measures that combine service access and service quality into one metric. However, operationalising effective coverage measurement in settings like Ethiopia requires understanding the context and feasibility with available data and resources. Thus, this study aimed to explore the demand for effective coverage measurement among Ethiopian key stakeholders and identify the actions needed to enhance its implementation in Ethiopia’s healthcare system. Methods An explorative, qualitative study design was employed, and data was collected through semi-structured interviews. Nineteen key informants from national level, three regions (Amhara, Oromia and Gambela), and one administrative city (Addis Ababa) in Ethiopia, participated in the study from March to April 2023. Key informants were identified using a snowballing approach. This study received ethical approval from the London School of Hygiene and Tropical Medicine and the Ethiopian Public Health Institute ethical review boards. Results There is a strong demand for effective coverage measurement for decision making on health care quality in the Ethiopian health sector. The demand is characterised either for individual patient care or for tracking health system performance; currently, there is limited clarity on the distinction between these two needs. A strong sentiment exists for the use of routine health information system data for this purpose, although stakeholders acknowledge the limitations of this data source. To advance the effective coverage measurement agenda in Ethiopia, respondents recommend building confidence in the quality of data sources, the need for more guidance on how to generate effective coverage measures, and the need for government leadership to coordinate these efforts. Conclusions The strong demand for effective coverage measures and the positive policy environment encourages further testing of effective coverage measurement in Ethiopia.

Use of family planning information by primary healthcare clinic managers in South Africa.

Routine family planning data in South Africa are managed using a routine health information system, which facilitates data collection, storage, processing, presentation and dissemination. Healthcare providers generate data daily as they carry out their duties. The information generated should be used for planning and evaluating health program performance and policy development. To explore the use of family planning information by primary healthcare (PHC) clinic managers in South Africa. A qualitative descriptive exploratory study was carried out in 11 PHC clinics in Tshwane District. Individual semi-structured interviews were conducted with 11 clinic managers. The data were analysed using a thematic analysis approach. Managers used the information to disseminate performance and feedback, monitor the program's performance and make decisions to improve the family planning service. However, they experienced challenges that hampered the effective use of the information. The use of family planning information is critical for improving the performance of the program. The clinics need sufficient skilled healthcare providers who are able to provide comprehensive family planning and generate accurate and reliable information that can be used to improve the service. Collaboration between the private and public sectors is critical in monitoring the program's performance.

“We don’t trust all data coming from all facilities”: factors influencing the quality of care network data quality in Ethiopia

ABSTRACT Background Good quality data are a key to quality health care. In 2017, WHO has launched the Quality of Care Network (QCN) to reduce maternal, newborn and stillbirth mortality via learning and sharing networks. Guided by the principle of equity and dignity, the network members agreed to implement the programme in 2017–2021. Objective This paper seeks to explore how QCN has contributed to improving data quality and to identify factors influencing quality of data in Ethiopia. Methods We conducted a qualitative study in selected QCN facilities in Ethiopia using key informant interview and observation methods. We interviewed 40 people at national, sub-national and facility levels. Non-participant observations were carried out in four purposively selected health facilities; we accessed monthly reports from 41 QCN learning facilities. A codebook was prepared following a deductive and inductive analytical approach, coded using Nvivo 12 and thematically analysed. Results There was a general perception that QCN had improved health data documentation and use in the learning facilities, achieved through coaching, learning and building from pre-existing initiatives. QCN also enhanced the data elements available by introducing a broader set of quality indicators. However, the perception of poor data quality persisted. Factors negatively affecting data quality included a lack of integration of QCN data within routine health system activities, the perception that QCN was a pilot, plus a lack of inclusive engagement at different levels. Both individual and system capabilities needed to be strengthened. Conclusion There is evidence of QCN’s contribution to improving data awareness. But a lack of inclusive engagement of actors, alignment and limited skill for data collection and analysis continued to affect data quality and use. In the absence of new resources, integration of new data activities within existing routine health information systems emerged as the most important potential action for positive change.

Development of novel composite data quality scores to evaluate facility-level data quality in electronic data in Kenya: a nationwide retrospective cohort study

BackgroundIn this evaluation, we aim to strengthen Routine Health Information Systems (RHIS) through the digitization of data quality assessment (DQA) processes. We leverage electronic data from the Kenya Health Information System (KHIS) which is based on the District Health Information System version 2 (DHIS2) to perform DQAs at scale. We provide a systematic guide to developing composite data quality scores and use these scores to assess data quality in Kenya.MethodsWe evaluated 187 HIV care facilities with electronic medical records across Kenya. Using quarterly, longitudinal KHIS data from January 2011 to June 2018 (total N = 30 quarters), we extracted indicators encompassing general HIV services including services to prevent mother-to-child transmission (PMTCT). We assessed the accuracy (the extent to which data were correct and free of error) of these data using three data-driven composite scores: 1) completeness score; 2) consistency score; and 3) discrepancy score. Completeness refers to the presence of the appropriate amount of data. Consistency refers to uniformity of data across multiple indicators. Discrepancy (measured on a Z-scale) refers to the degree of alignment (or lack thereof) of data with rules that defined the possible valid values for the data.ResultsA total of 5,610 unique facility-quarters were extracted from KHIS. The mean completeness score was 61.1% [standard deviation (SD) = 27%]. The mean consistency score was 80% (SD = 16.4%). The mean discrepancy score was 0.07 (SD = 0.22). A strong and positive correlation was identified between the consistency score and discrepancy score (correlation coefficient = 0.77), whereas the correlation of either score with the completeness score was low with a correlation coefficient of -0.12 (with consistency score) and -0.36 (with discrepancy score). General HIV indicators were more complete, but less consistent, and less plausible than PMTCT indicators.ConclusionWe observed a lack of correlation between the completeness score and the other two scores. As such, for a holistic DQA, completeness assessment should be paired with the measurement of either consistency or discrepancy to reflect distinct dimensions of data quality. Given the complexity of the discrepancy score, we recommend the simpler consistency score, since they were highly correlated. Routine use of composite scores on KHIS data could enhance efficiencies in DQA at scale as digitization of health information expands and could be applied to other health sectors beyondHIV clinics.

Dynamic zero-COVID policy and healthcare utilization patterns in China during the Shanghai COVID-19 Omicron outbreak

BackgroundIn April 2022, an outbreak of the SARS-CoV-2 virus Omicron variant in Shanghai precipitated an extensive lockdown. We assessed changes in healthcare utilization during this outbreak and investigated the relationship between the stringency of mitigation strategies and disruptions in healthcare utilization.MethodsUsing provincial-level data from routine health information systems covering all hospitals across Mainland China, we conducted an interrupted time series analysis to examine changes in healthcare utilization during the Shanghai outbreak. Linear regression was used to evaluate the direction and magnitude of the association between the relative changes in the move-out movement index, a proxy for the stringency in population-level mitigation strategies, and the estimated relative changes in healthcare utilization.ResultsOverall, there were 22.9 billion outpatient visits and 1.2 billion discharged inpatients during the study period from January 2016 to May 2022, including 9.1 billion (39.7%) and 0.46 billion (38.2%) in the post-COVID-19 period (January 2020–May 2022), respectively. From March through May 2022, the outbreak resulted in an accumulative loss of 23.5 million (47%) outpatient visits and 0.6 million (55%) discharged inpatients in Shanghai, and a loss of 150.3 million (14%) outpatient visits and 3.6 million (7%) discharged inpatients in other regions. We find that for every 10-percentage point reduction in the relative change of move-out index, a 2.7 (95% CI: 2.0–3.4) percentage point decline in the relative change of outpatient visits, and a 4.3 (95% CI: 3.5–5.2) percentage points decline in the relative change of inpatient discharges.ConclusionsThe Shanghai COVID-19 Omicron outbreak associates with a substantial reduction in outpatient visits and inpatient discharges within Shanghai and other regions in China. The stringency of the COVID-19 lockdown policies associates with more profound reductions in healthcare utilization.

Use of routine health information systems data in developing and monitoring district and facility health plans: a scoping review

BackgroundRoutine Health Information Systems data should be used in a systematic and institutionalised manner to support the making of plans, the monitoring of plans and in supportive supervision. To explore to what extent there is discussion about the linkage between planning, monitoring and supervision of sub-national programs using routine data we conducted a scoping review. The review question was: How are routine health information systems used in developing and monitoring health plans at district and facility level?MethodsFrom a search of Ovid Medline (all), EMBASE and Web of Science along with a review of grey literature and involving a number of key stakeholders in identifying any missing resources a total of over 2200 documents were reviewed and data from 13 documents were extracted.ResultsOverall, there are many descriptions of how to implement and strengthen systems, ways to assess and improve data availability and quality, tools to improve the data use context, training in data use and mechanisms to involve stakeholders and strengthen infrastructure. However, there are gaps in examples of routine health data being used in the development, monitoring and supervision of plans at district and facility level.ConclusionsThere appears to be no institutionalised obligation of planners to monitor plans, very little guidance on how to practically monitor programs and minimal discussion about how to use the routinely available data to supportively supervise the implementation of the plans. To overcome these shortcomings, we recommend that practical procedures to ensure linkage of existing district plans to regular monitoring of priority programs are institutionalised, that mechanisms for making managers institutionally accountable for monitoring and supervising these plans are put in place, and that practical guidelines for linking plans with routine health information system data and regular monitoring and supportive supervision are developed.

The impact of the COVID-19 pandemic on trends in stillbirths, under-5 and maternal mortality in Brazil: Excess deaths and regional inequalities.

Despite the proliferation of studies on the impact of the coronavirus disease 2019 (COVID-19) pandemic, there is less evidence on the indirect death toll compared to the health system and service provision disruptions. We assessed the impact of the COVID-19 pandemic on national and regional trends and differences in stillbirths, under-5 and maternal deaths in Brazil. We used the nationwide routine health information system data from January 2017 to December 2021, to which we applied descriptive and advanced mixed effects ordinary least squared regression models to measure the percent change in mortality levels during the COVID-19 pandemic (March 2020 to December 2021). We carried out counterfactual analyses comparing the observed and expected mortality levels for each type of mortality at national and regional levels. Stillbirths increased 4.8% (3.1% in 2020 and 6.2% in 2021) and most noticeably maternal deaths increased 71.6% (35.3% in 2020 and 103.3% in 2021) over the COVID-19 period. An opposite pattern was observed in under-5 mortality, which dropped -10.2% (-12.5% in 2020 and -8.1% in 2021). We identified regional disparities, with a higher percent increase in stillbirths observed in the Central-West region and in maternal deaths in the South region. Based on pre-pandemic trends and expected number of deaths in the absence of the COVID-19, we observed increases in stillbirths and maternal deaths and reductions in under-5 deaths during the pandemic. The months with the highest number of deaths (stillbirths and maternal deaths) coincided with the months with the highest mortality from COVID-19. The increase in deaths may also have resulted from indirect effects of the pandemic, such as unavailability of health services or even reluctance to go to the hospital when necessary due to fear of contagion. In Brazil, the COVID-19 outbreak and subsequent restrictions had a detrimental impact on stillbirths and maternal deaths. Even before the pandemic, mortality trends highlighted pre-existing regional inequalities in the country's health care system. Although there were some variations, increases were observed in all regions, indicating potential weaknesses in the health system and inadequate management during the pandemic, particularly concerning pregnant and postpartum women.

Malaria vaccine coverage estimation using age-eligible populations and service user denominators in Kenya

BackgroundThe World Health Organization approved the RTS,S/AS01 malaria vaccine for wider rollout, and Kenya participated in a phased pilot implementation from 2019 to understand its impact under routine conditions. Vaccine delivery requires coverage measures at national and sub-national levels to evaluate progress over time. This study aimed to estimate the coverage of the RTS,S/AS01 vaccine during the first 36 months of the Kenyan pilot implementation.MethodsMonthly dose-specific immunization data for 23 sub-counties were obtained from routine health information systems at the facility level for 2019–2022. Coverage of each RTS,S/AS01 dose was determined using reported doses as a numerator and service-based (Penta 1 and Measles) or population (projected infant populations from WorldPop) as denominators. Descriptive statistics of vaccine delivery, dropout rates and coverage estimates were computed across the 36-month implementation period.ResultsOver 36 months, 818,648 RTSS/AS01 doses were administered. Facilities managed by the Ministry of Health and faith-based organizations accounted for over 88% of all vaccines delivered. Overall, service-based malaria vaccine coverage was 96%, 87%, 78%, and 39% for doses 1–4 respectively. Using a population-derived denominator for age-eligible children, vaccine coverage was 78%, 68%, 57%, and 24% for doses 1–4, respectively. Of the children that received measles dose 1 vaccines delivered at 9 months (coverage: 95%), 82% received RTSS/AS01 dose 3, only 66% of children who received measles dose 2 at 18 months (coverage: 59%) also received dose 4.ConclusionThe implementation programme successfully maintained high levels of coverage for the first three doses of RTSS/AS01 among children defined as EPI service users up to 9 months of age but had much lower coverage within the community with up to 1 in 5 children not receiving the vaccine. Consistent with vaccines delivered over the age of 1 year, coverage of the fourth malaria dose was low. Vaccine uptake, service access and dropout rates for malaria vaccines require constant monitoring and intervention to ensure maximum protection is conferred.

Estimating the effects of COVID-19 on essential health services utilization in Uganda and Bangladesh using data from routine health information systems.

Since March 2020, the coronavirus disease 2019 (COVID-19) pandemic has been a major shock to health systems across the world. We examined national usage patterns for selected basic, essential health services, before and during the COVID-19 pandemic in Uganda and Bangladesh, to determine whether COVID-19 affected reporting of service utilization and the use of health services in each country. We used routine health information system data since January 2017 to analyze reporting and service utilization patterns for a variety of health services. Using time series models to replicate pre-COVID-19 trajectories over time we estimated what levels would have been observed if COVID-19 had not occurred during the pandemic months, starting in March 2020. The difference between the observed and predicted levels is the COVID-19 effect on health services. The time trend models for Uganda and Bangladesh closely replicated the levels and trajectories of service utilization during the 38 months prior to the COVID-19 pandemic. Our results indicate that COVID-19 had severe effects across all services, particularly during the first months of the pandemic, but COVID-19 impacts on health services and subsequent recovery varied by service type. In general, recovery to expected levels was slow and incomplete across the most affected services. Our analytical approach based on national information system data could be very useful as a form of surveillance for health services disruptions from any cause leading to rapid responses from health service managers and policymakers.

Utilization of health data and associated factors among department heads in public health facilities in Eastern Ethiopia: A cross-sectional study in Harari region

IntroductionEven though the information generated by routine health information systems is an essential element in the process of transforming the health sector, the information is systematically under-utilized by the health workers. Hence, this study was aimed to assess the utilization level and associated factors of routine health information system data among department heads in Eastern Ethiopia. Methods:The cross-sectional study design was conducted among heads of departments in the health facilities of Harari region. The source populations and the study populations were all department heads. The data were collected by standardized tools through interviews, observations, and document reviews. The data were entered into Epi Data version 3.1 and then exported to SPSS version 25 for analysis. Multivariable logistic regression was performed to identify the associated factors and P-value < 0.05 was used to declare the statistically significant association. Result:Of the respondents, 51.8 % live in urban, 82.4 % participated in performance review meeting, 61.7 % received feedback, 80.6 % engage in HIS, and 91 % feel responsible to HIS. Routine health information system data utilization among department heads was 177 (79.7 %); 95 % CI: [73.8 %, 84.8 %] in the Harari region. Factors associated with data utilization were urban residence (AOR = 2.891; 95 %CI: 1.147–7.286), getting feedback (AOR = 3.136; 95 %CI: 1.311–7.499), active engagement in health information system activities (AOR = 2.560; 95 %CI: 1.010–6.490), participation in performance review meeting (AOR = 3.847; 95 %CI: 1.563–9.464), and feeling responsibility (AOR = 3.727; 95 %CI: 1.071–12.961). Conclusion:Level of data use in this study was higher than the one in other studies in Ethiopia. Residence, feedback, level of engagement in health information system activities, sense of responsibility towards health information system, and performance review meeting were the determinants of data utilization. Important attention should be given by the officials at various levels to expand the information communication technology infrastructures and strengthen the feedback system.

Title: Identifying barriers to the production and use of routine health information in Western Province, Zambia.

Recent decades of improvements to routine health information systems (RHISs) in low and middle-income countries (LMICs) have increased the volume of health data collected. However, countries continue to face several challenges with quality production and use of information for decision-making at sub-national levels, limiting the value of health information for policy, planning, and research. Improving the quality of data production and information use is thus a priority in many LMICs to improve decision-making and health outcomes. This qualitative study identified the challenges of producing and using routine health information in Western Province, Zambia. We analyzed interview responses 37 health and social sector professionals at the national, provincial, district, and facility levels to understand the barriers to using data from the Zambian health management information system (HMIS). Respondents raised several challenges that we categorized into four themes: governance and health system organization; geographic barriers; technical and procedural barriers; and challenges with human resource capacity and staff training. Staff at the facility and district levels were arguably the most impacted by these barriers as they are responsible for much of the labour to collect and report routine data. However, facility and district staff had the least authority and ability to mitigate the barriers to data production and information use. Expectations for information use should therefore be clearly outlined for each level of the health system. Further research is needed to understand to what extent the available HMIS data address the needs and purposes of the staff at facilities and districts.

Qualitative Review of National Nutrition Surveillance Systems in the Eastern Mediterranean Region.

This study focused on the functional nutrition surveillance systems in eight Arab countries; namely Kuwait, Morocco, Oman, Palestine, Saudi Arabia, Sudan, Syria, and Yemen. The study's analysis involved utilizing primary data collected from both published and unpublished reports. Additionally, a structured checklist was employed to gather information from all countries involved in the study. Furthermore, interviews were conducted with the EMRO offices to gain deeper insights into the challenges, if any, that these nutrition surveillance systems face in functioning optimally. All countries use health facilities as a basic source of data for their nutrition surveillance, some countries triangulate their nutrition surveillance reports with data from other sources of information such as community or school surveys. Identified nutrition surveillance approaches are closely split between those who operate in stable settings and use routine health information systems (Morocco, Saudi Arabia, Oman, and Kuwait) and other countries that operate in fragile settings; for example, Yemen, Syria, Palestine, and Sudan struggle to provide early warning reports for rapid nutritional responses. Nutrition surveillance systems that utilize existing health information systems are the most sustained in the EMRO region. However, by integrating data from multiple sources, such as health facilities, surveys, and population censuses, countries can provide a holistic view of the nutritional situation, enhance their response to any emergency, and can leverage the infrastructure and resources already in place for health data collection and reporting. Collaboration between countries in the region through sharing experiences and success stories is important in order to reach a standardized system that can be implemented in different settings.

Process and Methodological Considerations for Observational Analyses of Vector Control Interventions in Sub-Saharan Africa Using Routine Malaria Data.

Progress in malaria control has stalled in recent years. With growing resistance to existing malaria vector control insecticides and the introduction of new vector control products, national malaria control programs (NMCPs) increasingly need to make data-driven, subnational decisions to inform vector control deployment. As NMCPs are increasingly conducting subnational stratification of malaria control interventions, including malaria vector control, country-specific frameworks and platforms are increasingly needed to guide data use for vector control deployment. Integration of routine health systems data, entomological data, and vector control program data in observational longitudinal analyses offers an opportunity for NMCPs and research institutions to conduct evaluations of existing and novel vector control interventions. Drawing on the experience of implementing 22 vector control evaluations across 14 countries in sub-Saharan Africa, as well as published and gray literature on vector control impact evaluations using routine health information system data, this article provides practical guidance on the design of these evaluations, makes recommendations for key variables and data sources, and proposes methods to address challenges in data quality. Key recommendations include appropriate parameterization of impact and coverage indicators, incorporating explanatory covariates and contextual factors from multiple sources (including rapid diagnostic testing stockouts; insecticide susceptibility; vector density measures; vector control coverage, use, and durability; climate and other malaria and non-malaria health programs), and assessing data quality before the evaluation through either on-the-ground or remote data quality assessments. These recommendations may increase the frequency, rigor, and utilization of routine data sources to inform national program decision-making for vector control.

Vaccine safety surveillance in Kenya using GAIA standards: A feasibility assessment of existing national and subnational research and program systems

BackgroundActive surveillance systems for monitoring vaccine safety among pregnant women address some of the limitations of a current passive surveillance approach utilized in low- and middle-income countries (LMIC). However, few active surveillance systems in LMIC exist.Our study assessed the feasibility of utilizing three existing data collection systems in Kenya for active surveillance of maternal immunization and to assess the applicability of Global Alignment of Immunization Safety Assessment in pregnancy (GAIA) case definitions that were initially developed for clinical trials within these systems. MethodsWe assessed applicability of GAIA case definition for maternal Tetanus Toxoid exposure, stillbirth, low birth weight, small for gestational age, Neonatal Invasive Blood Stream Infection (NIBSI), prematurity and neonatal death in two routine web-based health information systems (Kenya EMR and DHIS-2), and a web-based population-based pregnancy research platform (ANCOV11Antenatal, Intrapartum and Postnatal care COVID-19 Surveillance Study (ANCOV).) in Kenya. ResultsAll three HIS were capable of reporting selected outcomes to varying degrees of GAIA certainty. The ANCOV platform was the most robust in collecting and collating clinical data for effective maternal pharmacovigilance. The utilization of facility- and district-aggregated data limits the usefulness of DHIS-2 in pharmacovigilance as currently operationalized. While the Kenya EMR contained individual level data and meets the key considerations for effective pharmacovigilance, it was used primarily for HIV care and treatment records in a small proportion of health facilities and would require additional resources to expand to all antenatal care facilities and to link maternal and infant records. DiscussionPopulation-based research studies may offer a responsive short-term option for implementing maternal vaccine pharmacovigilance in LMICs. However, the foundation exists for long-term capacity building within the national health electronic data systems to provide this critical service as well as ensure participation of the country in international studies on maternal vaccine safety.

Routine Health Information System Utilization and Its Associated Factors among Healthcare Professionals in Debre Berhan Town, Ethiopia

Background. A routine health information system (RHIS) is a system that records, stores, and processes health data in order to enhance healthcare decision-making. However, systematic use of health information is still not used for program decisions in developing nations, particularly in Ethiopia. Objective. Identifying regular utilization of health information systems and related factors among healthcare workers in Debre Berhan Town health facilities, North Shoa, Amhara, Ethiopia, 2022. Methods. A facility-based cross-sectional study was conducted from February 22 to September 22, 2022 in Debre Berhan Town, Ethiopia. Data from participants were gathered using a self-administered, pretested structured questionnaire by employing a systematic random sample technique among healthcare workers at public health facilities. Data were entered into EpiData version 3.1 and analyzed using SPSS. We employed multivariable logistic regression and descriptive statistics. Variables with a p-value &lt; 0.05 were considered statistically significant factors. Hosmer–Lemeshow goodness of fit was used to check the adequacy of the model. Result. A total of 383 workers were study participants with 100% response rate. The mean age was 32 (±5.23). Utilization of routine health information among health professionals was 42.6%. Type of institution (AOR = 0.56), complexity of RHIS) (AOR = 2.19), training on HMIS (AOR = 9.35), and feeling guilty of not accomplishing their performance (AOR = 2.96) were found significantly associated with routine health information utilization (RHIU). Conclusion. Utilization of RHIS among the health professionals was low. Type of institution, complexity of RHIS, data management skills, training on HMIS, and feeling guilty of not accomplishing their target performance were factors related to RHIS utilization. Comprehensive training and improve data management skills are highly recommended for improving RHIU.

Area-specific covid-19 effects on health services utilization in the Democratic Republic of the Congo using routine health information system data

BackgroundSince March 2020, the COVID-19 pandemic has shocked health systems worldwide. This analysis investigated the effects of the pandemic on basic health services utilization in the Democratic Republic of the Congo (DRC) and examined the variability of COVID effects in the capital city Kinshasa, in other urban areas, and in rural areas.MethodsWe estimated time trends models using national health information system data to replicate pre-COVID-19 (i.e., January 2017–February 2020) trajectories of health service utilization, and then used those models to estimate what the levels would have been in the absence of COVID-19 during the pandemic period, starting in March 2020 through March 2021. We classified the difference between the observed and predicted levels as the effect of COVID-19 on health services. We estimated 95% confidence intervals and p-values to examine if the effect of the pandemic, nationally and within specific geographies, was statistically significant.ResultsOur results indicate that COVID-19 negatively impacted health services and subsequent recovery varied by service type and by geographical area. COVID-19 had a lasting impact on overall service utilization as well as on malaria and pneumonia-related visits among young children in the DRC. We also found that the effects of COVID-19 were even more immediate and stronger in the capital city of Kinshasa compared with the national effect. Both nationally and in Kinshasa, most affected services had slow and incomplete recovery to expected levels. Therefore, our analysis indicates that COVID-19 continued to affect health services in the DRC throughout the first year of the pandemic.ConclusionsThe methodology used in this article allows for examining the variability in magnitude, timing, and duration of the COVID effects within geographical areas of the DRC and nationally. This analytical procedure based on national health information system data could be applied to surveil health service disruptions and better inform rapid responses from health service managers and policymakers.

Effects of primary healthcare reform on routine health information systems (RHISs): a mixed-methods study in Lesotho

Background and objectiveThe Ministry of Health of Lesotho and Partners In Health piloted the Lesotho National Primary Health Care Reform (LPHCR) from July 2014 to June 2017 to improve quality...

Early effects of COVID-19 on maternal and child health service disruption in Mozambique.

After the World Health Organization declared COVID-19 a pandemic, more than 184 million cases and 4 million deaths had been recorded worldwide by July 2021. These are likely to be underestimates and do not distinguish between direct and indirect deaths resulting from disruptions in health care services. The purpose of our research was to assess the early impact of COVID-19 in 2020 and early 2021 on maternal and child healthcare service delivery at the district level in Mozambique using routine health information system data, and estimate associated excess maternal and child deaths. Using data from Mozambique's routine health information system (SISMA, Sistema de Informação em Saúde para Monitoria e Avaliação), we conducted a time-series analysis to assess changes in nine selected indicators representing the continuum of maternal and child health care service provision in 159 districts in Mozambique. The dataset was extracted as counts of services provided from January 2017 to March 2021. Descriptive statistics were used for district comparisons, and district-specific time-series plots were produced. We used absolute differences or ratios for comparisons between observed data and modeled predictions as a measure of the magnitude of loss in service provision. Mortality estimates were performed using the Lives Saved Tool (LiST). All maternal and child health care service indicators that we assessed demonstrated service delivery disruptions (below 10% of the expected counts), with the number of new users of family planing and malaria treatment with Coartem (number of children under five treated) experiencing the largest disruptions. Immediate losses were observed in April 2020 for all indicators, with the exception of treatment of malaria with Coartem. The number of excess deaths estimated in 2020 due to loss of health service delivery were 11,337 (12.8%) children under five, 5,705 (11.3%) neonates, and 387 (7.6%) mothers. Findings from our study support existing research showing the negative impact of COVID-19 on maternal and child health services utilization in sub-Saharan Africa. This study offers subnational and granular estimates of service loss that can be useful for health system recovery planning. To our knowledge, it is the first study on the early impacts of COVID-19 on maternal and child health care service utilization conducted in an African Portuguese-speaking country.

Associations between the stringency of COVID-19 containment policies and health service disruptions in 10 countries

BackgroundDisruptions in essential health services during the COVID-19 pandemic have been reported in several countries. Yet, patterns in health service disruption according to country responses remain unclear. In this paper, we investigate associations between the stringency of COVID-19 containment policies and disruptions in 31 health services in 10 low- middle- and high-income countries in 2020.MethodsUsing routine health information systems and administrative data from 10 countries (Chile, Ethiopia, Ghana, Haiti, Lao People’s Democratic Republic, Mexico, Nepal, South Africa, South Korea, and Thailand) we estimated health service disruptions for the period of April to December 2020 by dividing monthly service provision at national levels by the average service provision in the 15 months pre-COVID (January 2019-March 2020). We used the Oxford COVID-19 Government Response Tracker (OxCGRT) index and multi-level linear regression analyses to assess associations between the stringency of restrictions and health service disruptions over nine months. We extended the analysis by examining associations between 11 individual containment or closure policies and health service disruptions. Models were adjusted for COVID caseload, health service category and country GDP and included robust standard errors.FindingsChronic disease care was among the most affected services. Regression analyses revealed that a 10% increase in the mean stringency index was associated with a 3.3 percentage-point (95% CI -3.9, -2.7) reduction in relative service volumes. Among individual policies, curfews, and the presence of a state of emergency, had the largest coefficients and were associated with 14.1 (95% CI -19.6, 8.7) and 10.7 (95% CI -12.7, -8.7) percentage-point lower relative service volumes, respectively. In contrast, number of COVID-19 cases in 2020 was not associated with health service disruptions in any model.ConclusionsAlthough containment policies were crucial in reducing COVID-19 mortality in many contexts, it is important to consider the indirect effects of these restrictions. Strategies to improve the resilience of health systems should be designed to ensure that populations can continue accessing essential health care despite the presence of containment policies during future infectious disease outbreaks.